AI/ML Engineer

UST is hiring an AI/ML Engineer to develop, evaluate, and deploy enterprise AI, machine learning, and intelligent automation solutions. The role spans rapid prototyping and customer problem-solving, reusable architectures and measurement frameworks, and production-grade generative AI, agentic systems, and machine learning applications.

Responsibilities

As an AI/ML Engineer, you will operate across several interconnected modes

Firefighter Mode When an urgent or ambiguous AI challenge emerges, you bring structure, speed, and technical judgment. Rapidly evaluate new AI tools, models, frameworks, and platforms for enterprise use cases

Build working prototypes and technical proofs of concept under compressed timelines. Investigate AI applications that are underperforming and identify issues across data, prompts, retrieval, orchestration, models, infrastructure, or user workflows

Validate claims related to productivity improvement, automation, quality gains, cost reduction, and time savings. Instrument AI applications to determine whether they are being used, whether they are working, and whether they are creating measurable value

Recommend whether an AI initiative should be scaled, redesigned, paused, or discontinued. Communicate findings clearly to technical teams, business stakeholders, customers, and senior leadership

Step into high-priority customer and delivery situations, reduce ambiguity, and drive them toward closure

Systems Thinker Mode Design reusable architectures for enterprise Generative AI, Agentic AI, and Machine Learning applications

Build measurement frameworks that work across AI use cases such as software engineering, analytics, customer support, recruiting, HR, supply chain, finance, insurance, and healthcare

Define outcome metrics covering adoption, productivity, task completion, quality, accuracy, latency, cost, user satisfaction, risk, and business value

Design data pipelines and instrumentation that capture agent traces, tool calls, token consumption, model performance, user activity, feedback signals, and workflow outcomes

Build dashboards, semantic models, evaluation workspaces, and self-service analytics that allow stakeholders to understand AI performance without depending on manual reporting

Establish standardized evaluation and observability patterns across a fragmented and rapidly changing AI technology landscape

Help shape how UST and its clients think about AI-enabled productivity, workforce augmentation, operating models, job responsibilities, and organizational transformation

Convert successful prototypes into reusable accelerators, reference architectures, and enterprise platforms

Builder Mode Develop Generative AI and Machine Learning applications that solve complex enterprise and customer problems. Build intelligent agents and multi-agent workflows with planning, reasoning, tool use, memory, context management, human review, and workflow orchestration

Design production-grade Retrieval-Augmented Generation systems, including document ingestion, parsing, chunking, embedding generation, indexing, hybrid retrieval, reranking, grounding, citation generation, and hallucination mitigation

Develop secure Python services and APIs using FastAPI, Flask, or comparable frameworks. Integrate Large Language Models with enterprise databases, APIs, document repositories, SaaS platforms, knowledge bases, and business applications

Build scalable pipelines for data preparation, training, fine-tuning, inference, evaluation, deployment, monitoring, and continuous improvement

Develop Machine Learning and Deep Learning solutions across natural language processing, computer vision, forecasting, classification, recommendation, and structured-data use cases

Fine-tune and adapt models using techniques such as PEFT, LoRA, instruction tuning, prompt tuning, and domain-specific optimization when justified

Apply prompt engineering and context engineering techniques to improve accuracy, consistency, safety, and task completion

Deploy AI applications using cloud-native architectures, containers, microservices, event-driven systems, and managed AI platforms

Ensure that AI systems meet enterprise requirements for security, privacy, reliability, scalability, governance, auditability, and Responsible AI

Benefits

Full-time, regular employees accrue a minimum of 10 days of paid vacation per year.

Full-time, regular employees receive 6 days of paid sick leave each year (pro-rated for new hires throughout the year).

Full-time, regular employees receive 10 paid holidays.

Full-time, regular employees are eligible for paid bereavement leave and jury duty.

Full-time, regular employees are eligible to participate in the Company’s 401(k) Retirement Plan with employer matching.

Full-time, regular employees and their dependents residing in the US are eligible for medical, dental, and vision insurance.

Full-time, regular employees receive Company-paid Employee Only basic life insurance, accidental death and disability insurance, and short- and long-term disability benefits.

Regular employees may purchase additional voluntary short-term disability benefits.

Regular employees may participate in a Health Savings Account (HSA) and a Flexible Spending Account (FSA) for healthcare, dependent child care, and/or commuting expenses as allowable under IRS guidelines.

Benefits offerings vary in Puerto Rico.

Full-time temporary employees receive 6 days of paid sick leave each year (pro-rated for new hires throughout the year).

Full-time temporary employees are eligible to participate in the Company’s 401(k) program with employer matching.

Full-time temporary employees and their dependents residing in the US are eligible for medical, dental, and vision insurance.

All US employees who work in a state or locality with more generous paid sick leave benefits than specified here will receive the benefit of those sick leave laws.

Company

Global digital transformation solutions provider

Aliso Viejo, California, USA

https://www.ust.com/

Founded in 1999

10001+ employees

Qualifications

Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related technical field, or equivalent practical experience

Three or more years of hands-on experience in Machine Learning, Artificial Intelligence, Data Engineering, Software Engineering, Generative AI, or a related area

Strong proficiency in Python and experience building production-quality software

Experience developing and deploying Machine Learning, Generative AI, or intelligent automation applications

Experience working with Large Language Models, AI agents, RAG systems, or modern AI development platforms

Strong understanding of Machine Learning fundamentals, model evaluation, data preparation, experimentation, and production deployment

Ability to take an AI use case from an ambiguous initial idea through architecture, implementation, evaluation, and deployment

Strong written and verbal communication skills, including the ability to present technical findings to leadership and nontechnical stakeholders

Demonstrated ownership, sound judgment, and the ability to move quickly without compromising critical quality, security, or governance requirements

Three or more years of hands-on experience in Machine Learning, Artificial Intelligence, Data Engineering, Software Engineering, Generative AI, or a related area.

Required skills

Python, Machine Learning, Generative AI, Agentic AI, Large Language Models, Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), Prompt Engineering, REST APIs

Experience

3+ years